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treatment effects
The term ‘treatment effect’ refers to the causal effect of a binary (0–1) variable on an
outcome variable of scientific or policy interest.
Economics examples include the effects
of government programmes and policies, such as those that subsidize training for
disadvantaged workers, and the effects of individual choices like college attendance. The
principal econometric problem in the estimation of treatment effects is selection bias,
which arises from the fact that treated individuals differ from the nontreated for reasons
other than treatment status per se.
Treatment effects can be estimated using social
experiments, regression models, matching estimators, and instrumental variables.
A ‘treatment effect’ is the average causal effect of a binary (0–1) variable on an outcome
variable of scientific or policy interest.
The term ‘treatment effect’ originates in a medical
literature concerned with the causal effects of binary, yesorno ‘treatments’, such as an
experimental drug or a new surgical procedure.
But the term is now used much more generally.
The causal effect of a subsidized training programme is probably the mostly widely analysed
treatment effect in economics (see, for example, Ashenfelter, 1978, for one of the first examples,
or Heckman and Robb, 1985 for an early survey).
Given a dataset describing the labour market
circumstances of trainees and a nontrainee comparison group, we can compare the earnings of
those who did participate in the programme and those who did not.
Any empirical study of
treatment effects would typically start with such simple comparisons.
We might also use
regression methods or matching to control for demographic or background characteristics.
In practice, simple comparisons or even regressionadjusted comparisons may provide
misleading estimates of causal effects.
For example, participants in subsidized training
programmes are often observed to earn less than ostensibly comparable controls, even after
adjusting for observed differences (see, for example, Ashenfelter and Card, 1985).
This may
reflect some sort of omitted variables bias, that is, a bias arising from unobserved and
uncontrolled differences in earnings potential between the two groups being compared.
In
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general, omitted variables bias (also known as selection bias) is the most serious econometric
concern that arises in the estimation of treatment effects.
The link between omitted variables
bias, causality, and treatment effects can be seen most clearly using the potentialoutcomes
framework.
Causality and potential outcomes
The notion of a causal effect can be made more precise using a conceptual framework that
postulates a set of potential outcomes that could be observed in alternative states of the world.
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 Fall '08
 Staff
 Economics, Econometrics, Regression Analysis, DI, military service, Causal Effects, treatment effects

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